Enhancing Heart Disease Prediction Through VGG16 and CapsNet

This research investigates the application of deep learning algorithms, specifically Capsule Networks and Convolutional Neural Networks (CNNs) based on VGG16, for the early prediction of cardiac issues. By analyzing a diverse dataset of health-related information, we aim to improve the accuracy of binary classification in predicting the presence or absence of heart disease. The study compares the performance of CapsNets and VGG16 CNNs, with VGG16 demonstrating superior accuracy at 99.8% and CapsNets achieving 98% precision. This research contributes to the advancement of predictive analytics in healthcare, enabling more accurate and timely diagnosis of cardiac conditions and paving the way for improved patient outcomes.